Abstract

Hidden Markov models (HMM) have been successfully applied to the tasks of
transmembrane protein topology prediction and signal peptide prediction.
In this paper we expand upon this work by making use of the more powerful
class of dynamic Bayesian networks (DBN). Our model, Philius,
is inspired
by a previously published HMM, Phobius, and combines a signal peptide
sub-model with a
transmembrane sub-model. We introduce a two-stage DBN decoder which
combines the power of posterior decoding with the grammar constraints
of Viterbi-style decoding.
Philius also provides protein type, segment, and topology
confidence metrics to aid in the interpretation of the
predictions.

We report a relative improvement of 13% over Phobius in full-topology
prediction accuracy on transmembrane proteins, and a sensitivity
and specificity of 0.96 in detecting signal peptides. We also show that
our confidence metrics correlate well with the observed precision.
In addition, we have made predictions on all 6.3 million proteins in
the Yeast Resource Center (YRC) database.